Gets the learning curve working.

machine-learning-ex5/ex5/ex5.m

@@ -54,7 +54,7 @@ fprintf(['Cost at theta = [1 ; 1]: %f '...
          '\n(this value should be about 303.993192)\n'], J);
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% =========== Part 3: Regularized Linear Regression Gradient =============
 %  You should now implement the gradient for regularized linear 
@@ -69,7 +69,7 @@ fprintf(['Gradient at theta = [1 ; 1]:  [%f; %f] '...
          grad(1), grad(2));
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 
 %% =========== Part 4: Train Linear Regression =============
@@ -94,7 +94,7 @@ plot(X, [ones(m, 1) X]*theta, '--', 'LineWidth', 2)
 hold off;
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 
 %% =========== Part 5: Learning Curve for Linear Regression =============

machine-learning-ex5/ex5/learningCurve.m

@@ -54,7 +54,12 @@ error_val   = zeros(m, 1);
 % ---------------------- Sample Solution ----------------------
 
 
+for i = 1:m
+    [theta] = trainLinearReg(X(1:i, :), y(1:i, :), lambda);
+    error_train(i) = linearRegCostFunction(X(1:i, :), y(1:i, :), theta, 0);  
+    error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);  
 
+end
 
 
 

machine-learning-ex5/ex5/linearRegCostFunction.m

@@ -33,7 +33,8 @@ J = temp1 + temp2;
 
 % =========================================================================
 
-t11 = sigmoid(X*theta) - y;
+% t11 = sigmoid(X*theta) - y;
+t11 = (X*theta) - y;
 t12 = repmat(t11, 1, size(X, 2));
 temp11 = (1/m) * sum(X .* t12);
 

machine-learning-ex5/ex5/sigmoid.m

@@ -3,5 +3,4 @@ function g = sigmoid(z)
 %   J = SIGMOID(z) computes the sigmoid of z.
 
 g = 1.0 ./ (1.0 + exp(-z));
-% g = z;
 end